docstr: NB: This is a meta LinearOperator, in the sense that evaluate can return
  a LinearOperator if the kernel being evaluated does so.
docstr_tok: ["NB",":","This","is","a","meta","LinearOperator",",","in","the","sense","that","evaluate","can","return","a","LinearOperator","if","the","kernel","being","evaluated","does","so","."]
code: def evaluate_kernel(self):
      """
      NB: This is a meta LinearOperator, in the sense that evaluate can return
      a LinearOperator if the kernel being evaluated does so.
      """
      x1 = self.x1
      x2 = self.x2
  
      with settings.lazily_evaluate_kernels(False):
          temp_active_dims = self.kernel.active_dims
          self.kernel.active_dims = None
          res = self.kernel(
              x1,
              x2,
              diag=False,
              last_dim_is_batch=self.last_dim_is_batch,
              **self.params,
          )
          self.kernel.active_dims = temp_active_dims
  
      # Check the size of the output
      if settings.debug.on():
          if res.shape != self.shape:
              raise RuntimeError(
                  f"The expected shape of the kernel was {self.shape}, but got {res.shape}. "
                  "This is likely a bug in GPyTorch."
              )
  
      return to_linear_operator(res)
  
imports: import functools
  from typing import Callable
  import torch
  from linear_operator import LinearOperator, to_linear_operator
  from linear_operator.utils.getitem import _noop_index
  from ..None import beta_features, settings
  from ..utils import deprecation
  from ..utils.memoize import cached
label_desc: Use image node_id 14 for calling the LazyEvaluatedKernelTensor obj's underlying member method code with example usage: obj.evaluate_kernel() and returns: to_linear_operator